# Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch LUKE model.""" import unittest from transformers import LukeConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( LukeForEntityClassification, LukeForEntityPairClassification, LukeForEntitySpanClassification, LukeForMaskedLM, LukeForMultipleChoice, LukeForQuestionAnswering, LukeForSequenceClassification, LukeForTokenClassification, LukeModel, LukeTokenizer, ) class LukeModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, entity_length=3, mention_length=5, use_attention_mask=True, use_token_type_ids=True, use_entity_ids=True, use_entity_attention_mask=True, use_entity_token_type_ids=True, use_entity_position_ids=True, use_labels=True, vocab_size=99, entity_vocab_size=10, entity_emb_size=6, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, num_entity_classification_labels=9, num_entity_pair_classification_labels=6, num_entity_span_classification_labels=4, use_entity_aware_attention=True, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.entity_length = entity_length self.mention_length = mention_length self.use_attention_mask = use_attention_mask self.use_token_type_ids = use_token_type_ids self.use_entity_ids = use_entity_ids self.use_entity_attention_mask = use_entity_attention_mask self.use_entity_token_type_ids = use_entity_token_type_ids self.use_entity_position_ids = use_entity_position_ids self.use_labels = use_labels self.vocab_size = vocab_size self.entity_vocab_size = entity_vocab_size self.entity_emb_size = entity_emb_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.num_entity_classification_labels = num_entity_classification_labels self.num_entity_pair_classification_labels = num_entity_pair_classification_labels self.num_entity_span_classification_labels = num_entity_span_classification_labels self.scope = scope self.use_entity_aware_attention = use_entity_aware_attention self.encoder_seq_length = seq_length self.key_length = seq_length self.num_hidden_states_types = 2 # hidden_states and entity_hidden_states def prepare_config_and_inputs(self): # prepare words input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) # prepare entities entity_ids = ids_tensor([self.batch_size, self.entity_length], self.entity_vocab_size) entity_attention_mask = None if self.use_entity_attention_mask: entity_attention_mask = random_attention_mask([self.batch_size, self.entity_length]) entity_token_type_ids = None if self.use_token_type_ids: entity_token_type_ids = ids_tensor([self.batch_size, self.entity_length], self.type_vocab_size) entity_position_ids = None if self.use_entity_position_ids: entity_position_ids = ids_tensor( [self.batch_size, self.entity_length, self.mention_length], self.mention_length ) sequence_labels = None token_labels = None choice_labels = None entity_labels = None entity_classification_labels = None entity_pair_classification_labels = None entity_span_classification_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) entity_labels = ids_tensor([self.batch_size, self.entity_length], self.entity_vocab_size) entity_classification_labels = ids_tensor([self.batch_size], self.num_entity_classification_labels) entity_pair_classification_labels = ids_tensor( [self.batch_size], self.num_entity_pair_classification_labels ) entity_span_classification_labels = ids_tensor( [self.batch_size, self.entity_length], self.num_entity_span_classification_labels ) config = self.get_config() return ( config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ) def get_config(self): return LukeConfig( vocab_size=self.vocab_size, entity_vocab_size=self.entity_vocab_size, entity_emb_size=self.entity_emb_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, use_entity_aware_attention=self.use_entity_aware_attention, ) def create_and_check_model( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): model = LukeModel(config=config) model.to(torch_device) model.eval() # test with words + entities result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, ) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual( result.entity_last_hidden_state.shape, (self.batch_size, self.entity_length, self.hidden_size) ) # test with words only result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_labels = self.num_entity_classification_labels model = LukeForMaskedLM(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, labels=token_labels, entity_labels=entity_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) if entity_ids is not None: self.parent.assertEqual( result.entity_logits.shape, (self.batch_size, self.entity_length, self.entity_vocab_size) ) else: self.parent.assertIsNone(result.entity_logits) def create_and_check_for_entity_classification( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_labels = self.num_entity_classification_labels model = LukeForEntityClassification(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, labels=entity_classification_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_entity_classification_labels)) def create_and_check_for_entity_pair_classification( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_labels = self.num_entity_pair_classification_labels model = LukeForEntityClassification(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, labels=entity_pair_classification_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_entity_pair_classification_labels)) def create_and_check_for_entity_span_classification( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_labels = self.num_entity_span_classification_labels model = LukeForEntitySpanClassification(config) model.to(torch_device) model.eval() entity_start_positions = ids_tensor([self.batch_size, self.entity_length], self.seq_length) entity_end_positions = ids_tensor([self.batch_size, self.entity_length], self.seq_length) result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, entity_start_positions=entity_start_positions, entity_end_positions=entity_end_positions, labels=entity_span_classification_labels, ) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.entity_length, self.num_entity_span_classification_labels) ) def create_and_check_for_question_answering( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): model = LukeForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_labels = self.num_labels model = LukeForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_labels = self.num_labels model = LukeForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, labels=token_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ): config.num_choices = self.num_choices model = LukeForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_attention_mask = attention_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_entity_ids = entity_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_entity_token_type_ids = ( entity_token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() ) multiple_choice_entity_attention_mask = ( entity_attention_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() ) multiple_choice_entity_position_ids = ( entity_position_ids.unsqueeze(1).expand(-1, self.num_choices, -1, -1).contiguous() ) result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_attention_mask, token_type_ids=multiple_choice_token_type_ids, entity_ids=multiple_choice_entity_ids, entity_attention_mask=multiple_choice_entity_attention_mask, entity_token_type_ids=multiple_choice_entity_token_type_ids, entity_position_ids=multiple_choice_entity_position_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "entity_ids": entity_ids, "entity_token_type_ids": entity_token_type_ids, "entity_attention_mask": entity_attention_mask, "entity_position_ids": entity_position_ids, } return config, inputs_dict @require_torch class LukeModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( LukeModel, LukeForMaskedLM, LukeForEntityClassification, LukeForEntityPairClassification, LukeForEntitySpanClassification, LukeForQuestionAnswering, LukeForSequenceClassification, LukeForTokenClassification, LukeForMultipleChoice, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": LukeModel, "fill-mask": LukeForMaskedLM, "question-answering": LukeForQuestionAnswering, "text-classification": LukeForSequenceClassification, "token-classification": LukeForTokenClassification, "zero-shot": LukeForSequenceClassification, } if is_torch_available() else {} ) test_resize_embeddings = True # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if pipeline_test_case_name in ["QAPipelineTests", "ZeroShotClassificationPipelineTests"]: return True return False def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): entity_inputs_dict = {k: v for k, v in inputs_dict.items() if k.startswith("entity")} inputs_dict = {k: v for k, v in inputs_dict.items() if not k.startswith("entity")} inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if model_class == LukeForMultipleChoice: entity_inputs_dict = { k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() if v.ndim == 2 else v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1, -1).contiguous() for k, v in entity_inputs_dict.items() } inputs_dict.update(entity_inputs_dict) if model_class == LukeForEntitySpanClassification: inputs_dict["entity_start_positions"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device ) inputs_dict["entity_end_positions"] = torch.ones( (self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device ) if return_labels: if model_class in ( LukeForEntityClassification, LukeForEntityPairClassification, LukeForSequenceClassification, LukeForMultipleChoice, ): inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class == LukeForEntitySpanClassification: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device, ) elif model_class == LukeForTokenClassification: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) elif model_class == LukeForMaskedLM: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) inputs_dict["entity_labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device, ) return inputs_dict def setUp(self): self.model_tester = LukeModelTester(self) self.config_tester = ConfigTester(self, config_class=LukeConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "studio-ousia/luke-base" model = LukeModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_masked_lm_with_word_only(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() config_and_inputs = (*config_and_inputs[:4], *((None,) * len(config_and_inputs[4:]))) self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_entity_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_entity_classification(*config_and_inputs) def test_for_entity_pair_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_entity_pair_classification(*config_and_inputs) def test_for_entity_span_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_entity_span_classification(*config_and_inputs) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_length = self.model_tester.seq_length entity_length = self.model_tester.entity_length key_length = seq_length + entity_length for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length + entity_length, key_length], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = self.model_tester.num_hidden_states_types self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length + entity_length, key_length], ) def test_entity_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) entity_hidden_states = outputs.entity_hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(entity_hidden_states), expected_num_layers) entity_length = self.model_tester.entity_length self.assertListEqual( list(entity_hidden_states[0].shape[-2:]), [entity_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_retain_grad_entity_hidden_states(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] entity_hidden_states = outputs.entity_hidden_states[0] entity_hidden_states.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(entity_hidden_states.grad) @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @require_torch class LukeModelIntegrationTests(unittest.TestCase): @slow def test_inference_base_model(self): model = LukeModel.from_pretrained("studio-ousia/luke-base").eval() model.to(torch_device) tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", task="entity_classification") text = ( "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped" " the new world number one avoid a humiliating second- round exit at Wimbledon ." ) span = (39, 42) encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt") # move all values to device for key in encoding: encoding[key] = encoding[key].to(torch_device) outputs = model(**encoding) # Verify word hidden states expected_shape = torch.Size((1, 42, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] ).to(torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) # Verify entity hidden states expected_shape = torch.Size((1, 1, 768)) self.assertEqual(outputs.entity_last_hidden_state.shape, expected_shape) expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]]).to(torch_device) torch.testing.assert_close(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_large_model(self): model = LukeModel.from_pretrained("studio-ousia/luke-large").eval() model.to(torch_device) tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large", task="entity_classification") text = ( "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped" " the new world number one avoid a humiliating second- round exit at Wimbledon ." ) span = (39, 42) encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt") # move all values to device for key in encoding: encoding[key] = encoding[key].to(torch_device) outputs = model(**encoding) # Verify word hidden states expected_shape = torch.Size((1, 42, 1024)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] ).to(torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) # Verify entity hidden states expected_shape = torch.Size((1, 1, 1024)) self.assertEqual(outputs.entity_last_hidden_state.shape, expected_shape) expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]]).to(torch_device) torch.testing.assert_close(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)